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How to train small Deep Research intelligence bodies? From architecture design to landing practice

itadol5j

In the area of AI, Deep Research is redefining the word “research”. As an practitioner, have you ever wondered how to train a small Deep Research intelligence that belongs to you? Today, we will look into this front-line topic in depth.

What's Deep Research intelligence?

Deep Research is an AI system capable of autonomous in-depth research. Unlike traditional search engines, it can:

  • Zenium Multi-source information integration: information collected and validated from different sources
  • Zenium In-depth reasoning analysis• Systemic analysis of complex issues
  • Zenium Structure Output: Generating professional studies
  • Zenium Diverse Optimization: Continuous improvement of research results based on feedback

• Core technology for training small Deep Research intelligence

1. (RAG) Retrieval enhancement generation

RAG is the cornerstone of Deep Research. By combining large language models with external knowledge bases, achieve:

  • Real-time knowledge update
  • Reducing hallucinogenic problems
  • Improving the accuracy of answers

2. Multistep reasoning framework

The research process requires multiple rounds of reasoning:

  • Problems decompose, sub-issue studies, integration of information, generation of conclusions

3. Tool mobilization capacity

Smart bodies need to be skilled in using tools:

  • 🔎 Search engine API
  • Academic database
  • Code Execution Environment
  • Data analysis tool

Training data preparation

Quality data is key to success:

  • Zenium Academic dissertation data setsPublic papers: arXiv, PubMed, etc. Library
  • Zenium High-quality web data: Selected authoritative website content
  • Zenium Dialogue interactive data: Proceedings of the study
  • Zenium Structured knowledgeKnowledge mapping, databases, etc.

Data processing points:

  • Weighting, cleaning, quality screening.
  • Area Experts
  • Build assessment baseline

Model fine-tuning policy

1. Basic model selection

  • It is recommended to use large open source models as bases: LaMA, Qwen, DeepSeek, etc.

2. Methodology for fine-tuning

  • Zenium LoRA fine tunes.: efficient parameters fine-tuned and costing reduced
  • Zenium All-parameter fine: Fit to a well-calculated scenario
  • Zenium RLHF: Enhanced learning based on human feedback to improve the quality of research

3. Training techniques

  • Phased training: generic and post-professional
  • Course learning: from simple to complex research tasks
  • Ad hoc excavation: focus on optimizing difficult samples

Evaluation and optimization

Assessment dimensions:

Dimensions Indicators Annotations
Precision F1, Recall Accuracy of information retrieval
Integrity Coverage Extent of coverage of research content
Depth Reasoning Score Depth of analytical reasoning
Readability Human Rating Manually assess output quality

Recommendations on land-based practices

Since childhood

  • Select a vertical field (e.g. financial, medical) as an entry Points

2. Closed rings Organisation Subaru.

  • Collect user feedback, optimize models, deploy on line, and continue over time Subaru.

3. Cost control

  • Using model distillation techniques to migrate large model capabilities to small models
  • Rational use of API to reduce the cost of reasoning

• Summary and outlook

The training of small Deep Research intelligents is a systematic engineering that requires:

  • • Clear operational objectives
  • • High-quality training data
  • Appropriate model structure
  • • Continuous optimization

As the open source ecology continues to improve, it is becoming more feasible to train its own Deep Research intelligence.

In the future, every company may have its own research assistant.